Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/understudy-ai/understudy/explore-unfamiliar-targetnpx skills add understudy-ai/understudy --skill explore-unfamiliar-targetgit clone --depth 1 https://github.com/understudy-ai/understudyWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/understudy-ai/understudy/explore-unfamiliar-target)<a href="https://agentmods.dev/skills/understudy-ai/understudy/explore-unfamiliar-target"><img src="https://agentmods.dev/badge/skills/understudy-ai/understudy/explore-unfamiliar-target.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00016 | $0.00281 |
| Opus 5 | $0.00008 | $0.00140 |
| Sonnet 5 | $0.00003 | $0.00056 |
| Haiku 4.5 | $0.00002 | $0.00028 |
Grade A, and why
explore-unfamiliar-target scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
explore-unfamiliar-target
Goal
Explore the unfamiliar target currently assigned to the run.
Operating Contract
- Work from the visible app state rather than assuming a fixed flow.
- Capture enough evidence for highlights, limitations, and a short brief.
- Prefer user-visible value over exhaustive settings exploration.
Inputs
- targetName
- artifactsRootDir
- analysisFocus
Outputs
- findings.md
- highlights.json
- limitations.json
- worker-summary.json
- screenshots/*
Budget
- maxMinutes=12
- maxActions=60
- maxScreenshots=12
Allowed Surfaces
- Only the surfaces assigned to the run
- Supporting workspace windows only when required
Stop Conditions
- Enough evidence exists for 2-3 highlights and 1 limitation.
- The budget is exhausted.
- A hard block such as authentication or missing permissions prevents further exploration.
Decision Heuristics
- Prefer first-run onboarding and core feature paths.
- Capture screenshots only when they materially improve the brief.
Failure Policy
- Escalate if payment is required.
- Escalate if personal account creation is required.
- Escalate if the app crashes repeatedly.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 62 lines · 16 tokens per session scan A b7594da01b31
explore-unfamiliar-target is a skill published in the GitHub repository understudy-ai/understudy (457 stars, last pushed 2mo ago), licensed MIT. It adds 16 tokens to every session and 281 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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